Facial expressions vary from the visible to the subtle. In recent years, the analysis of micro-expressions − a natural occurrence resulting from the suppression of one's true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting microexpressions in long videos becomes increasingly challenging when intertwined with normal or macro-expressions. In this paper, we propose a shallow optical flow three-stream CNN (SOFTNet) model to predict a score that captures the likelihood of a frame being in an expression interval. By fashioning the spotting task as a regression problem, we introduce pseudo-labeling to facilitate the learning process. We demonstrate the efficacy and efficiency of the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art performance is achieved on CAS(ME)2 with equally promising results on SAMM Long Videos.
@article{arxiv.2106.06489,
title = {Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos},
author = {Gen-Bing Liong and John See and Lai-Kuan Wong},
journal= {arXiv preprint arXiv:2106.06489},
year = {2021}
}
Comments
Accepted for publication in ICIP2021. 9 pages, including 3 pages of supplemental notes